Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

  1. Why does it make sense to have a log10 scale on x axis?

A logarithmic scale on the x axis makes it so that the two axes do not increase at the same rate. To elaborate, when the y axis increase with one value, the x axis will increase with a factor of ten. This is useful when working with values with a large range. It makes the plots more legible.

  1. Who is the outlier (the richest country in 1952 - far right on x axis)?
gapminder %>% 
  filter(year == 1952) %>% 
  select(country, gdpPercap) %>% 
  arrange(desc(gdpPercap))
## # A tibble: 142 x 2
##    country        gdpPercap
##    <fct>              <dbl>
##  1 Kuwait           108382.
##  2 Switzerland       14734.
##  3 United States     13990.
##  4 Canada            11367.
##  5 New Zealand       10557.
##  6 Norway            10095.
##  7 Australia         10040.
##  8 United Kingdom     9980.
##  9 Bahrain            9867.
## 10 Denmark            9692.
## # … with 132 more rows

The richest country in 1952 was Kuwait.

I used the filter() function to only search within the year 1952. Then I used the select() function to isolate the two columns named “country” and “gdpPercap”. Lastly, I used the arrange() function so that it would list from the largest to the lowest value in gdpPercap by using the desc() (descend) function.

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

  1. Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10() +
  labs(title = "Life expectancy by GDP per capita in 2007",
       subtitle = "sorted by continent and population of country",
       x = "GDP per capita",
       y = "Life expectancy")

I changed the labels using the labs() function. However, I have not yet found a way to change the scientific notations.

  1. What are the five richest countries in the world in 2007?
gapminder %>%
  filter(year == 2007) %>% 
  select(country, gdpPercap) %>% 
  arrange(desc(gdpPercap))
## # A tibble: 142 x 2
##    country          gdpPercap
##    <fct>                <dbl>
##  1 Norway              49357.
##  2 Kuwait              47307.
##  3 Singapore           47143.
##  4 United States       42952.
##  5 Ireland             40676.
##  6 Hong Kong, China    39725.
##  7 Switzerland         37506.
##  8 Netherlands         36798.
##  9 Canada              36319.
## 10 Iceland             36181.
## # … with 132 more rows

I used the same function from before but changed the year. The richest five countries in 2007 are Norway, Kuwait, Singapore, United States and Ireland.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

  2. Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.

 anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10(labels = scales::comma) +
  labs(title = 'Year: {frame_time}', x = "GDP per capita", y = "Life expectancy" ) +
  transition_time(year)
anim3

I used the labs() function to make a title and change the labels. I added “labels = scales::comma)” to the scale_x_log10() to change the scientific notations to whole numbers. I have not yet found a way to change the labels or scientific notations for “pop”.

  1. Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

I think it would be interesting to look at specific countries. I’m going use the Scandinavian countries Denmark, Norway and Sweden as an example.

Question: What are the difference in life expectancy and GDP per capita between the countries of Scandinavia in the period 1952-2007?

First I’m going to make a new table where I use the filter function isolate these three countries.

scandinavia <- filter(gapminder, country == "Denmark" | country == "Norway" | country == "Sweden")

Then I am going to create the animation. I am going to change the logarithmic x axis since the GDP per capita between the countries did not seem that different.

anim4 <- ggplot(scandinavia, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = country)) +
  scale_x_continuous(labels = scales::comma) +
  labs(title = "Life expectancy and GDP per capita in Scandinavia throughout the years", subtitle = 'Year: {frame_time}', x = "GDP per capita", y = "Life expectancy") +
  transition_time(year)
anim4

As we can see Denmark has the lowest life expectancy of the three countries. Norway is the smallest country, however it does have the highest GDP almost all throughout the period - especially towards the end. Sweden is the largest country of the three and its population have the overall highest life expectancy throughout most of the period even though it has the lowest GDP per capita overall. However, the difference between the GDP per capita of Denmark and Sweden do not seem that different when compared to Norway. However, Perhaps the difference seem greater in the visualisation than it actually is. For instance, the y axis is only marked for every 2.5 years.